reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction
reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction
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DOI：
10.1186/s12859-019-2645-4
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发表时间：
2019-03-29
影响因子：
3
通讯作者：
Sese, Jun
中科院分区：
文献类型：
--
作者：
Kawaguchi, Risa;Kiryu, Hisanori;Sese, Jun
背景近年来，新一代测序技术被应用于RNA二级结构的检测，即高通量RNA结构分析（HTS），许多不同的方法被用于以单核苷酸分辨率检测RNA的结构。然而，现有的计算分析在很大程度上依赖于实验方法来生成数据，这导致与统计上合理的比较或结合使用不同的HTS methods.ResultsHere获得的结果的困难，我们介绍了一个统计框架，reactIDR，它可以应用于使用多个HTS方法获得的实验数据。使用这种方法，核苷酸被分为三个结构类别，环，茎/背景，和未映射。reactIDR使用具有隐马尔可夫模型的不可再现发现率（IDR）来准确区分在重复HTS实验中获得的真实和虚假信号，并且能够结合期望最大化算法和监督学习来进行有效的参数优化。现实生活中的HTS数据的分析结果表明，reactIDR的核糖体RNA干/环结构的分类时，使用个人和综合HTS数据集的准确性最高，其结果对应的最好的三维structure.ConclusionsWe已经开发出一种新的软件，reactIDR，从HTS分析数据集的干/环区域的预测。对于rRNA结构分析，通过使用再现性标准，reactIDR在不同的数据集上具有强大的准确性，这表明它有可能增加现有HTS数据集的价值。reactIDR可在https://github.com/carushi/reactIDR上公开获得。
BackgroundRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.ResultsHere, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.ConclusionsWe have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.